Methods and Devices for Accurately Classifying Cardiac Activity

ABSTRACT

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.

RELATED APPLICATIONS

The present application claims the benefit of and priority to U.S.Provisional Patent Application No. 61/051,332, filed May 7, 2008 andtitled METHODS AND DEVICES FOR IDENTIFYING AND CORRECTING OVERDETECTIONOF CARDIAC EVENTS, and is a Continuation-In-Part of U.S. patentapplication Ser. No. 12/399,914, filed Mar. 6, 2009 and titled METHODSAND DEVICES FOR ACCURATELY CLASSIFYING CARDIAC ACTIVITY, the disclosuresof which are incorporated herein by reference.

The present application is related to U.S. patent application Ser. No.12/399,901, filed Mar. 6, 2009 and titled ACCURATE CARDIAC EVENTDETECTION IN AN IMPLANTABLE CARDIAC STIMULUS DEVICE, which claims thebenefit of and priority to U.S. Provisional Patent Application No.61/034,938, filed Mar. 7, 2008, the disclosures of which areincorporated herein by reference.

FIELD

The present invention relates generally to implantable medical devicesystems that sense and analyze cardiac signals. More particularly, thepresent invention relates to implantable medical devices that capturecardiac signals within an implantee's body in order to classify cardiacactivity as likely benign or malignant.

BACKGROUND

Implantable cardiac devices typically sense cardiac electrical signalsin an implantee and classify the implantee's cardiac rhythm asnormal/benign or malignant. Illustrative malignant rhythms may includeventricular fibrillation and/or ventricular tachyarrhythmia. Theaccuracy with which an implantable medical device analyzes capturedsignals determines how well it makes therapy and other decisions.

New and/or alternative methods and devices for cardiac signal analysisare desired.

SUMMARY

Some illustrative embodiments relate to the use of correlation analysisto identify overdetection of cardiac events. In one example, aHigh-Low-High pattern of correlation relative to a template is sought.The template may be a static template, it may be a representation of arecent captured event, or it may be an average of several recentcaptured events. In another example, multiple boundaries for Highcorrelation are defined, wherein a first, higher boundary (requiringgreater correlation) allows identification of overdetection based on asmaller set of detected events than a second, lower boundary. In oneembodiment, a shorter sequence of High-Low-High is sufficient with thefirst boundary, while a longer sequence of five or more (for example,eight) alternating events is required for the second boundary. Inanother embodiment, definitions of High and Low correlation are adaptedto the particular signals by using average values for subsets ofdetected event correlations to establish boundaries.

In another embodiment, correlation analysis is performed multiple timesfor a given template and detected event by shifting the alignment of thetemplate and the detected event to maximize the correlation score of theanalysis. Such shifting may adjust the alignment by one or more samplesaway from the identified fiducial points for analysis. In anotherembodiment, stored templates are modified in order to accommodatechanges in morphology for selected portions of the signal. In yetanother embodiment, multiple features of the template and/or signal areidentified and multiple correlation scores are calculated using severaldifferent features as alignment points.

When identified, overdetection can be corrected by modifying stored datain order to impact rate analysis. In one such embodiment, datacorrection is inhibited if the intervals surrounding a likelyoverdetection are longer than a predetermined threshold. In someembodiments, overdetection correction is inhibited if interval analysisrelating to a likely overdetection indicates that it is unlikely to be aparticular type of overdetection. In one such embodiment, the intervalssurrounding a likely overdetection are analyzed to determine whether anaccepted formula for estimating expected QT intervals is met and, ifnot, the method determines that the likely overdetection is not aT-wave, and so no data correction occurs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram for an illustrative method of identifyingoverdetection and taking corrective action;

FIG. 2 shows an illustrative implantable cardiac stimulus system;

FIG. 3A shows an example using correlation analysis to identifyoverdetection;

FIG. 3B illustrates method steps for an illustrative example includingrate correction;

FIG. 4 shows an example of inter-event correlation comparisons;

FIG. 5 shows another example of inter-event correlation comparisons;

FIG. 6 shows an analytical approach to short series and long seriescorrelation analysis;

FIGS. 7A-7B illustrate examples of applying the analytical approach ofFIG. 6 to series of correlation analyses;

FIGS. 8A-8B illustrate examples of tailoring correlation analysis toobserved levels of correlation to a template;

FIG. 9 illustrates another method of aligning captured signal tocorrelation analysis templates;

FIG. 10 shows another method of storing and applying a template forcorrelation analysis;

FIGS. 11-12 illustrate a method of inhibiting correlation analysisidentification of an overdetection;

FIG. 13 illustrates more methods for inhibiting correlation analysisidentification of an overdetection;

FIGS. 14A-14B show application of a method illustrated in FIG. 13;

FIG. 15 shows a method of shock analysis for identifying shockabledetected events and treatable rhythms; and

FIG. 16 illustrates a method of calculating the correlation between acaptured signal and a template.

DETAILED DESCRIPTION

The following detailed description should be read with reference to thedrawings. The drawings, which are not necessarily to scale, depictillustrative embodiments and are not intended to limit the scope of theinvention. Some of the following examples and explanations includereferences to issued patents and pending patent applications. Thesereferences are for illustrative purposes and are not intended to limitthe present invention to the particular methods or structures from thosereferenced patents and patent applications.

Unless implicitly required or explicitly stated, the methods below donot require any particular order of steps. It should be understood thatwhen the following examples refer to a “current event,” in someembodiments, this means the most recently detected cardiac event isbeing analyzed. However, this need not be the case, and some embodimentsperform analysis that is delayed by one or more detections and or afixed period of time. Choices shown regarding use ofrectified/unrectified signals are merely illustrative, and may bechanged if desired.

The nomenclature used herein indicates that a signal is sensed by animplantable cardiac device system, events are detected in the sensedsignal, and cardiac activity is classified by use of the detected events(detections). Rhythm classification includes the identification ofmalignant rhythms, such as ventricular fibrillation or certaintachyarrhythmias, for example. Implantable therapy systems maketherapy/stimulus decisions in reliance upon the classification of thecardiac rhythm.

In an illustrative example, a detected event is detected by comparingreceived signals to a detection threshold, which is defined by adetection profile. Any suitable detection profile may be used. Detectedevents are separated by intervals. Several intervals can be used togenerate an average interval across a selected number of intervals, fromwhich cardiac rate can be calculated. For example, four, eight orsixteen intervals may be used to estimate cardiac event rate as afunction of average interval.

A cardiac electrogram includes several portions (often referenced as“waves”) that, according to well known convention, are labeled withletters including P, Q, R, S, and T, each of which corresponds toparticular physiological events. It is typical to design detectionalgorithms to sense the R-wave, though any portion of the cardiac cycle,if repeatedly detected, can be used to generate a beat rate. Ifmorphology (shape) analysis is used in addition to heart rate, thesystem may capture and/or analyze the portion of the cycle that includesthe Q, R and S waves, referred to as the QRS complex. Other portions ofthe patient's cardiac cycle, such as the P-wave and T-wave, are oftentreated as artifacts that are not sought for the purpose of estimatingheart rate, though this need not be the case.

Typically, for purposes of ascertaining rate each cardiac cycle iscounted only once. Overdetection (such as a double or triple detection)may occur if the device declares more than one detected event within asingle cardiac cycle. Overdetection may occur if more than one portionof a single cardiac cycle is detected, or if noise causes an event to bedeclared when no cardiac event has taken place, for example, due toexternal therapy or noise, pacing artifact, skeletal muscle noise,electro-therapy, etc.

If one cardiac cycle takes place and a detection algorithm declaresmultiple detected events, overdetection has occurred. If the heart rateis then calculated by counting each of these detections, overcountingoccurs. Calculated heart rates may be used alone or in combination withother factors to classify cardiac rhythms as malignant or benign.Overcounting in reliance on overdetected events can result inerroneously high rate calculation. Miscalculation of heart rate can leadto incorrect rhythm classification and therapy decisions. Some of theseconcepts are further discussed and developed in U.S. patent applicationSer. No. 12/399,914, titled METHODS AND DEVICES FOR ACCURATELYCLASSIFYING CARDIAC ACTIVITY, and U.S. patent application Ser. No.12/399,901, titled ACCURATE CARDIAC EVENT DETECTION IN AN IMPLANTABLECARDIAC STIMULUS DEVICE.

FIG. 1 is a process flow diagram for an illustrative method ofidentifying overdetection and taking corrective action. The illustrativemethod begins with event detection 10, where a received cardiac signalis captured and compared to a detection threshold until the receivedsignal crosses the detection threshold, resulting in declaration of adetected event.

Next, the method performs an overdetection identification step 12. Thismay include one or more of several analysis methods including, asillustratively shown, morphology analysis 14, interval analysis 16 andwide QRS analysis 18. Following overdetection identification 12, if oneor more overdetections are identified, the method corrects data, asshown at 20. If no data correction is needed at step 20, this step maybe bypassed.

Finally, the method includes a therapy decision, as shown at 22. Atherapy decision 22 may classify a cardiac rhythm of the implantee anddetermines whether/when therapy is to be delivered. The method theniterates to event detection 10.

The therapy decision 22 may include one or more of several forms ofanalysis. In one illustrative example, individual detected events aremarked as shockable or non-shockable and an X-out-of-Y counter ismaintained to determine whether the overall cardiac rhythm meritstherapy. The marking of individual events as shockable or non-shockablemay take several forms, including rate-based and/or morphology baseddeterminations, or combinations thereof. FIG. 15, below, provides anillustrative example. Further examples are also discussed in U.S. Pat.No. 6,754,528, entitled APPARATUS AND METHOD OF ARRHYTHMIA DETECTION INA SUBCUTANEOUS IMPLANTABLE CARDIOVERTER/DEFIBRILLATOR, and U.S. Pat. No.7,330,757 entitled METHOD FOR DISCRIMINATING BETWEEN VENTRICULAR ANDSUPRAVENTRICULAR ARRHYTHMIAS, the disclosures of which are incorporatedherein by reference.

Therapy decision 22 may also take into account the persistence of amalignant condition. Some illustrative examples are shown in US PatentApplication Publication Number 2006/0167503 titled METHOD FOR ADAPTINGCHARGE INITIATION FOR AN IMPLANTABLE CARDIOVERTER-DEFIBRILLATOR, thedisclosure of which is incorporated herein by reference. Other methodsmay be used as a part of the therapy decision 22.

FIG. 2 shows an illustrative implantable medical device and implantlocation. More particularly, an illustrative subcutaneous-only system isshown in FIG. 2. The subcutaneous system is shown relative to a heart40, and includes a canister 42 coupled to a lead 46. The canister 42preferably houses operational circuitry for performing analysis ofcardiac activity and for providing a therapy output. The operationalcircuitry may include batteries, input/output circuitry, powercapacitors, a high-voltage charging module, a controller, memory,telemetry components, etc., as known in the art.

Electrodes are disposed at locations throughout the system including,for example, an electrode 44 on the canister 42, and electrodes 48, 50,52 on lead 46. The electrodes 44, 48, 50, 52 may take any suitable formand can be made of any suitable material. For example, the canisterelectrode 44 may be an isolated button electrode or it may be a regionor surface of the canister 42, and the electrodes 48, 50, 52 on lead 46may be coil electrodes, ring electrodes, or other structures known inthe art.

The electrodes 44, 48, 50, 52 define a plurality of sensing vectors suchas V1, V2, V3 and V4. If desired, one or more vectors V1, V2, V3, and V4may be chosen as a default sensing vector, for example, as discussed inUS Patent Application Publication Number 2007-0276445 titled SYSTEMS ANDMETHODS FOR SENSING VECTOR SELECTION IN AN IMPLANTABLE MEDICAL DEVICE.Other uses of multiple vectors are shown, for example, in U.S. Pat. No.7,392,085 titled MULTIPLE ELECTRODE VECTORS FOR IMPLANTABLE CARDIACTREATMENT DEVICES. Another embodiment considers posture in vectoranalysis, for example, as discussed in US Patent Application PublicationNumber 2008-0188901 titled SENSING VECTOR SELECTION IN A CARDIACSTIMULUS DEVICE WITH POSTURAL ASSESSMENT. Multiple sensing vectors maybe analyzed, sequentially or in combination, as desired.

Therapy may be applied using any chosen pair of electrodes. Anillustrative example uses the can electrode 44 and the coil electrode 52to apply therapy. Other electrode combinations may be used. Therapy mayinclude monophasic or multiphasic defibrillation, cardioversion and/orpacing.

The present invention is not limited to any particular hardware, implantlocation or configuration. Instead, it is intended as an improvementupon any implantable cardiac therapy system. Some embodiments may alsobe used in a monitoring system to either control the monitoringfunctions (including annunciation and/or data recording) and/or to testthe suitability of the data analysis to a particular configuration,condition or patient.

Some illustrative examples can associate with an external programmer 54configured to communicate with the implanted device for variouspurposes, including, for example and without limitation, one or more ofthe following: device testing; upload new/revised software; modifyprogrammable parameters such as detection or therapy settings; determinethe status of device operation, battery life, or lead integrity; enableor disable functionality; and/or download data relating to theimplantee's condition, prior data capture, or treatment. Any suitablecommunication method may be used, such as various protocols and hardwarewidely known in the art.

FIG. 2 omits several anatomical landmarks. The illustrative system shownmay be implanted beneath the skin, outside of the ribcage of theimplantee. The location illustratively shown would place the canister 42at approximately the left axilla of the implantee, level with thecardiac apex, with the lead 46 extending medially toward the xiphoid andthen toward the head of the implantee along the left side of thesternum. One illustrative example uses a method/system as shown incommonly assigned US Patent Application Publication Number 2006-0122676entitled APPARATUS AND METHOD FOR SUBCUTANEOUS ELECTRODE INSERTION.Other illustrative subcutaneous systems and locations are shown incommonly assigned U.S. Pat. Nos. 6,647,292, 6,721,597 and 7,149,575.

The present invention may also be embodied in systems having variousimplant configurations including, for example, other subcutaneous-only,vascular-only, and/or transvenous implantation configurations/locations.The canister 42 may be placed in anterior, lateral, and/or posteriorpositions including, without limitation, axillary, pectoral, andsub-pectoral positions, as well as placements on either the left orright side of the implantee's torso and/or in the abdomen. Entirelyintravascular implantation of the system has also been proposed. Thecanister 42 and lead 46 may be placed in any of a number of suitableconfigurations including anterior-posterior combinations, anterior-onlycombinations, transvenous placement, or other vascular placements. Aunitary system may omit lead 46 and instead include all electrodes onthe canister 42.

FIG. 3A shows an example using correlation analysis to identifyoverdetection. “Correlation analysis” as used herein can take severalforms. One illustrative example is shown in FIG. 16. Referring to FIG.16, a captured signal 500 undergoes analog-to-digital conversion 502 toyield a time ordered series of samples {S1 . . . S9} that form a sampled(and usually digital) representation of the signal, as indicated at 504.The example in FIG. 16 is simplified for illustrative purposes as thenumber of samples for a given signal may be greater than nine. Forexample, in one illustrative embodiment, the captured signal 500 isabout 160 milliseconds long, covering 41 samples captured at 256 Hz.Other durations and/or sampling frequencies may be selected. The signalcan be windowed to approximately the QRS width, though this is notrequired.

The signal representation is compared to a template using correlationanalysis 506. The template is shown as comprising a series of samplevalues {T1 . . . T9}. Prior to comparison, or as part of the comparison,the signal representation or template is scaled such that the largestpeaks of the two data sets are equal in amplitude. One example ofcorrelation analysis is correlation waveform analysis. Other examplesare widely known in the art.

A simple version of correlation analysis is shown graphically in FIG.16: the largest sample or peak of the signal representation is alignedwith the peak of the template and the surrounding samples are comparedto one another as shown at 508. Because the peaks are already scaled tobe equal, there is no difference at the peak, but the surroundingsamples may differ. Differences between the signal representation andthe template are shown in cross-hatching.

Next a correlation score may be calculated as shown at 510. The sum ofthe absolute values of the differences between (scaled) samples of thesignal representation and samples of the template is calculated anddivided by the total area under the template. The quotient is subtractedfrom one, yielding a correlation score 512. If the correlation score isclose to one, then the area of difference is small relative to the areaunder the template, indicating high correlation. Other methods forcalculating correlation are known in the art and may be substituted;that shown in FIG. 16 is simply an example. For example, a weighted CWAmay apply a weighting factor to individual sample differences in afashion as shown in commonly assigned, copending U.S. Patent App. Pub.No. 2008-0077030.

Returning to FIG. 3A, individual events are detected by applying adetection profile 70 to a signal 72. The detection profile 70 includes arefractory period 74 followed by a constant threshold period 76 and adecay period 78. Other shapes may be used for the detection profile 70.

The signal 72 has R-waves and T-waves highlighted. In the example shown,the T-waves are large relative to the R-waves. The refractory periodsshown in cross-hatching over both R-waves and T-waves indicates thateach R-wave and each T-wave is being treated as a detected event. As aresult, for each cardiac cycle, the detection profile 70 is detectingtwo events. This is one example of overdetection.

In the illustrative example, each of the individual detections is alsobeing treated to correlation analysis relative to a template that isbased on an R-wave. The results of the correlation analysis are plottedat 80. Plot 80 includes boundaries for “High” and “Low” correlation. Inthe example, each “X” indicates the correlation score for each detectedevent. A High-Low-High pattern of correlation scores occurs as shown at82. In the example, each High-Low-High sequence leads to a conclusionthat “Low” scoring detected events are overdetected. As a result, asshown, the “Low” scoring detected event will be discarded when aHigh-Low-High pattern is found. In a numeric example, “High” is definedas greater than 52% correlation, while “Low” is defined as less than 25%correlation, when calculated using the form shown at 510 in FIG. 16.Other values and analytical methods can be used.

FIG. 3B illustrates method steps for an illustrative example includingrate correction. Once a morphology overdetection pattern is found, asindicated at 90, one or more overdetections are identified, as shown at92. Next, event intervals and/or rate are recalculated, as shown at 94.

For example, as shown at 96, a series of detections of R and T waves mayresult in a set of interval calculations of 225 ms (R to T) and 300 ms(T to R), which yields an average interval of 263 ms. An averageinterval of 263 milliseconds leads to a rate of about 229beats-per-minute, which would be a treatable tachy-arrhythmia in manypatients. However, when the T-waves are identified as overdetections andthe intervals on either side of the T-waves are combined, as shown at98, the intervals average 525 milliseconds. The rate can be recalculatedto about 114 beats-per-minute, avoiding possible defibrillation,cardioversion or pacing that could result without the data correction.

FIG. 4 shows an example of inter-event correlation comparisons. Aninter-event comparison is a comparison in which two individual detectedevents are compared to one another. The comparison may take the form ofa correlation analysis, or it may make use of some other type ofanalysis such as wavelet transform, principal component analysis (PCA),etc., to consider the similarity between two detected events. In wavelettransform or PCA comparisons, the similarity of the results of datacompression into wavelet or PCA outputs can be compared. For example,the similarity and/or order of eigenvalue outputs of PCA, or thesimilarity of the wavelet coefficients resulting from a wavelettransformation can be compared in a qualitative or quantitative manner.

In the example shown in FIG. 4, a correlation analysis is performed. Inthe example, as shown at 108, correlation scores are characterized asLow, Middle, or High. The “High” score zone indicates strong confidencethat the compared signals are of the same character (for example, if oneevent is an R-wave, so is the other), while “Low” scores indicate thatthe compared signals are very different from one another. The “Middle”zone is intended to capture those signals that are similar but that donot create strong confidence that the two signals are of the samecharacter. For example, in a patient who undergoes a rate-dependentmorphology change (such as a rate-induced bundle block), capturedR-waves may not highly correlate to a stored static template but likelyfall into the Middle range relative to the template. In another example,a monomorphic VT likely has High or Middle inter-event correlationbetween R-waves, and Middle correlation between T-waves, while apolymorphic VT would show Middle or Low correlation between R-waves.

If desired, fuzzy logic may be applied. The use of a “Middle Zone”suggests this. For example, rather than simple “High” and “Low”characterizations, additional categories may be provided. Further, aprevious measurement may be used to inform a subsequent characterizationof a marginally similar or dissimilar signal.

As shown at 100, a series of events N, N-1, N-2 and N-3 are consideredas a group, with the N^(th) detection compared to each of N-1, N-2 andN-3 via correlation analysis. The results of inter-event comparisons andcomparisons to a static template are shown in a table at 102. Theinter-event comparison results are shown at 104, and include orderedresults for comparison of a given event to three prior events. Table 102shows results for events N, N-1, N-2 and N-3. The results of theinter-event comparisons show that for any given event X, the correlationto X-2 is higher than for X-1 or X-3. This may indicate a pattern ofdouble detection based on increased correlation between alternatingevents.

In the illustrative example, comparisons to a static, normal sinusrhythm template may be performed as well. Illustrative results are shownat 106. The alternating static template results, Low-Middle-Low-Middle .. . are suggestive of possible overdetection, but because the likelyR-waves do not Highly correlate, strong confidence does not result basedon static template alone. However, when taken in combination with theinter-event comparison information, there is significant confidence thatsome events are overdetections. An applicable rule set may be asfollows:

1) Alternating Low-High-Low for N when compared to N-1, N-2 and N-3, and

2) Alternating Low-High-Low for N-2 when compared to N-3, N-4 and N-5.

Conclusion: Treat N-1 and N-3 as T-waves.

A further, confirmatory rule may be:

3) At least “Medium” correlation for N and N-2 to static template.

Another approach is to apply only rules 1) and 3), while marking onlythe N-1 as an overdetection in response to the rule set being met. Onceone or more events are marked as overdetections, they may be treated inthe manner shown in FIG. 3B, above.

FIG. 5 shows another example of inter-event correlation comparisons.Here the captured signal is triple-detected, as shown at 120. In thisinstance, the Nth detection is compared to each of N-1, N-2, N-3 andN-4. The inclusion of four individual comparisons may further assist indistinguishing a triple detection from a double detection, although someembodiments stop at three comparisons.

The results are shown in the table at 124. For each set of comparisons,there are three Low correlations, and one Middle or one Highcorrelation. It is likely that with triple detection, some detectionswill have a low correlation in each comparison. An illustrative rule setis as follows:

1. Nth event has High correlation to the N-3 event;

2. N-1 and N-2 events have Low correlations to the Nth event; and

3. N-1 and N-2 events have Low correlations to the Static Template.

If these three conditions are met, then N-1 and N-2 may be discarded.Further conditions may be added. For example, the static templatecharacteristics of N and/or N-3 may be considered as well, for example:

4. Nth and N-3 events have Middle or High Correlation to StaticTemplate.

Then if all of 1-4 are met, N-1 and N-2 may be discarded and theinterval from N to N-3 calculated and used in rate analysis.

In a further example, the widths of each event may also be considered,for example using this fourth condition:

5. N-1 and N-2 events are wider than a Width Threshold.

The width threshold may be set as desired; in one example the WidthThreshold is in the range of 100-140 ms. This Width Threshold rule maybe applied as an added layer to any determination that an event is to bediscarded as an overdetection. In another example, the polarities may beconsidered:

6. N-1 and N-2 each share the same polarity.

Polarity may be defined, for example, by reference to the majority ofsignal samples for an event, as the polarity of the sample having thegreatest magnitude in the event, or by determination of which extreme,the most positive or least positive, in the event occurs first.

If desired, interval coupling may be added as another condition:

7. The combined interval N to N-3 less than Duration.

Where “Duration” is in the range of 800-1200 ms. This condition, andvariants thereof, is also explained in association with FIGS. 11-13 and14A-B below.

FIG. 6 shows an analytical approach to short series and long seriescorrelation analysis. FIG. 6 shows a plot 140 for plotting thecorrelation scores for a series of detected events. The correlationscores, shown as X's, are plotted against lines 144 and 146 that definea wide band 148, and lines 150, 152 that define a narrow band 154.

The wide band 148 is applied to identify an overdetection when there aretwo detected events with scores above line 144 separated by a singledetected event with a score below line 146, for example as shown in FIG.7A. The narrow band is applied to identify overdetection(s) when aseries of consecutive detections alternate above line 150 and below line152, for example as shown in FIG. 7B. Numbers are shown for eachthreshold for illustrative purposes; these numbers may use correlationas a percentage.

The narrower band 154 applies a less stringent standard than the widerband 152 with regard to the correlation scores, and therefore moreevents are analyzed before making a decision to discard low scoringevents. In one illustrative example, events are not discarded using thenarrow band 154 until the 8 event pattern shown in FIG. 7B is met, atwhich point one to four of the low scoring events are discarded, withintervals around each discarded event being corrected. Subsequent tomeeting the pattern in this initial step, only the newest low scoringevent would be discarded. For analytical purposes, previously discardedevents are used to determine whether the 8-consecutive-outside rule ismet, even if those events are excluded from rate calculations. Anotherembodiment uses only five events, looking for a High-Low-High-Low-Highsequence using the narrower band 154 and, if such a sequence is found,one or both of the Low scoring events is discarded.

The examples in FIGS. 6 and 7A-7B indicate numbers, with 50% and 20%correlations bordering the wide band 148 and 40% and 25% bordering thenarrow band 154. These numbers are merely illustrative. In one example,these numbers are applied by scaling the formula shown at 510 in FIG. 16to a percentage basis.

FIGS. 8A-8B illustrate examples of tailoring correlation analysis toobserved levels of correlation to a template. Referring to FIG. 8A, aplot of correlation scores for comparing a template to a series ofevents is shown at 158. For purposes of identifying double detections, amean correlation score is calculated for the odd numbered events.Clustering of the odd numbered events is then analyzed by determiningwhether the odd numbered events all fall within a predefined distancefrom the mean, for example, using the standard deviation of the set, orusing a fixed distance. If the odd numbered events all fall within thepredefined distance from the mean, the separation of the mean from a Lowboundary is calculated. If the separation is greater than apredetermined threshold, then it is determined that the odd numberedevents demonstrate monotonicity supporting a presumption that the oddnumbered events are QRS complex detections. If monotonicity of the oddnumbered events is identified, one or more of the even numbered eventsthat fall below the low threshold are marked as overdetections.

In another embodiment, before any of the even numbered events are markedas overdetections, they are all analyzed to determine whether clusteringof the even numbered events has taken place, again using the mean ofthose events. Rather than separation of the odd-numbered event mean froma low boundary, separation between the even and odd event means iscalculated to establish groupings of the events. In this embodiment,overdetection markers are applied only when sufficient clustering of theeven-numbered events appears.

FIG. 8B shows another example in which the marking of overdetections istailored to correlation scores to a static template. Here, the averagecorrelation score for a set of 10 events is calculated. A “blank” bandis then established around the average correlation score. For example,the blank band may be defined as +/−15%. Other “blank band” sizes may beused.

In the example of FIG. 8B, high scores are defined as those scores thatfall above the blank band, and low scores are those falling below theblank band. If a pattern of High-Low-High appears around the blank band,then overdetection can be identified and one or more of the Low scoringevents is marked as an overdetection.

Instead of a static template, the analysis shown by FIGS. 8A-8B may alsobe applied using a recently detected event as the template forcomparison. The analysis noted for FIGS. 8A-8B may use calculation ofthe mean/average, or it may use some other predictor of a center-pointfor signals including the mode, median or other mathematical operation.

A further use of the inter-event comparisons shown here may be in thedetermination of whether a Shockable rhythm is occurring. Stimulusdelivery is often used to address polymorphic conditions, such asPolymorphic Ventricular Tachycardia and Ventricular Fibrillation.Monomorphic conditions such as Monomorphic Ventricular Tachycardia (MVT)can be treated, but MVT does not always require the most energetictreatments. For example, MVT may be treated using anti-tachycardiapacing (ATP) in place of defibrillation or cardioversion, as ATP usesless energy and may be less traumatic to the patient. Patterns ofcorrelation can be used to distinguish monomorphic arrhythmias frompolymorphic arrhythmias. For example, an ongoing pattern as shown inFIG. 7A or 7B, or even FIG. 6, in which high correlations areconsistently found, can be used to delay therapy, if desired.

In another example, a pattern as shown in FIG. 8A may be furtheranalyzed by determining the size of the standard deviation for theclustered high scores. If the clustered high scores are based on astatic template and show a low standard deviation, this may indicate amonomorphic condition. In some embodiments, particularly if ATP is notavailable, therapy may be inhibited until the monomorphic conditionbreaks down into a more polymorphic condition.

In one example, a system uses a tiered correlation analysis to identifytreatable arrhythmias. In the example, a simple, single eventcorrelation analysis using a static template is executed until a patternas shown in FIG. 8A appears. Such a pattern then triggers multipleinter-event comparisons as shown in FIGS. 4-5. Then, if the inter-eventcomparisons show likely overdetection, interval data may be corrected.Further, if inter-event comparisons show a monomorphic condition,therapy may be inhibited.

FIG. 9 illustrates methods for aligning and realigning a captured signalto a correlation analysis template. The correlation analysis template isshown at 200, with a signal shown at 202. The correlation analysistemplate 200 may be a static template or it may represent a singledetected event or average of several recently detected events.

As noted in FIG. 16, correlation analysis typically uses a fiducialpoint as an alignment guide for an ordered series of template values andsignal samples. In the example of FIG. 9, a base alignment point isidentified as the sample of each of the template 200 and the signal 202having the greatest magnitude. A series of comparisons are then made,beginning with a base aligned comparison, shown at 210, andsingle-sample shifts to the right, shown at 212, and the left, shown at214. The shift one right correlation 212 is worse than the correlationscore for the base comparison 210, and so the result of the shift oneright correlation 212 is discarded. The shift one left correlation 214yields a higher correlation score than the aligned correlation 210, sothe result of the base correlation 210 is discarded, and another shiftleft correlation is calculated as shown at 216, this time offsetting thealignment points by two samples. The result at 216 shows lessercorrelation than the shift-one-left correlation at 214, and so theprocess stops and uses the correlation score calculated for theshift-one-left correlation 214 as the correlation score for the signal202.

When performing the shifting to the right and/or left, scaling of thesignal to the template may be modified as well. For example, if scalingis initially performed by comparing the peak for the signal to the peakfor the template and then equalizing the two, on shifting, the peak forthe signal may instead be scaled to the point it aligns to in thetemplate after shifting has occurred.

The method demonstrated in FIG. 9 may help to correct for noise ormisalignment based on sampling artifact, slew rate, etc., that may causethe peak alignment point of the sample 202 to be less than optimal. Themethod includes calculating the correlation score when the fiducialpoints are aligned and also when the fiducial points are misaligned byone or more samples in each of two directions until a maximumcorrelation score is found. Limits may be placed, as desired, on thenumber of samples to shift to the left or right. In another embodiment,several (for example, one base, one, two, and three to the left, one,two and three to the right) scores are automatically calculated and thebest is chosen.

In another embodiment highlighted in FIG. 9, plural alignment points canbe defined for the template 200. Some examples include the QRS onset,the maximum amplitude, the maximum amplitude in the opposite polarity ofthe maximum amplitude (note the maximum amplitudes are indicated by eachbeing a turning point where dV/dt=0), the maximum slope point betweenthe two major peaks (shown as dV/dt=MAX, etc.). By identifying theanalogous points in the signal, the method can determine whether use ofdifferent possible alignment points would provide different correlationanalysis outcomes. For example, the default may be to use the maximumamplitude point of the entire signal, but it may be that some cardiacevents can be aligned instead using the maximum slope point in themonotonic segment that follows the maximum amplitude point.

FIG. 10 shows another method of storing and applying a template forcorrelation analysis. In this example, the signal forming a basis for atemplate is shown at 230. For the illustrative example, when thetemplate is formed an interpolation region is defined between thepositive peak and the negative peak of the signal 230. As a result, thestored template takes the form shown at 240: The template 240 matchesthe template signal 230 for regions before the positive peak and afterthe negative peak, but is flexible between the two peaks, as indicatedby the dashed line at 242. The positive peak, in the example shown, isthe largest magnitude peak in the template, and so it is used forscaling the template to a captured signal.

Alignment to a sample 232 is then performed as shown at 244. Thetemplate is adjusted such that the positive and negative peaks arealigned with the captured signal, with a linear interpolationtherebetween. Outside of the positive and negative peaks, the templatecontinues to match the signal as shown at 230, however, the duration andslope between the positive and negative peaks are adjusted to match thecaptured event. The adjustment shown in FIG. 10 may avoid the difficultyof a static template being fixed in duration for a patient whose QRSwidth is affected by rate. The adjustment made may be limited in orderto avoid excessively widening the template.

In another example, more than two template points are identified andlinear interpolation may be used between them. For example, a templatemay be composed of five values each having a relative amplitude andrelative location. When a detected event is to be compared to thetemplate, the width and peak amplitude of the detected event are used toscale each of the values of the template, with linear interpolationbetween the template points.

FIGS. 11-12 illustrate a method of inhibiting data correction followingidentification of a likely overdetection.

As shown in FIG. 11, a QRS complex occurs at 260, followed by apremature ventricular contraction (PVC) shown at 262, following byanother QRS complex at 264. The PVC is characterized, in this example,by a low correlation to the template. Thus, a High-Low-High correlationpattern appears, similar to that shown above in FIG. 3A. Some exampleswould therefore discard the PVC 262. Analytically, however, discardingthe PVC 262 may be unnecessary since it is not actually an overdetectedevent. Further, the intervals around the PVC 262 are both greater than500 milliseconds. Even without data correction, the average of the twointervals would yield an event rate of about 103 beats-per-minute, arate that would not threaten to cause unnecessary therapy. Thus the datacorrection would not improve rhythm specificity in the device, whilereducing beat sensitivity.

FIG. 12 illustrates a method that would avoid discarding a PVC 262 asshown in FIG. 11. Based on detected events 270, the method determines,as shown at 272, whether a correlation score sequence appears that wouldsupport a finding of double detection (DD) or overdetection. If not, themethod ends, as no data correction is about to ensue. If the result from272 is a “Yes,” the method next includes determining whether the newinterval that would result from data correction would be greater than apredetermined threshold, as shown at 274. In the illustrative example,the threshold is 1000 ms (60 beats-per-minute), though this number ismerely illustrative. Some likely thresholds are in the range of 750-1200milliseconds.

In another example, the order of analysis is reversed, and theoverdetection analysis does not take place unless the calculated rate ishigh (often 150 bpm or more), or unless the intervals that could beaffected are short enough to pass the applied test. In anotherembodiment, individual intervals are compared to a threshold (forexample, in the range of 400-600 ms) and, if the individual intervalsboth exceed the threshold, then no interval combining occurs. In yetanother example, the threshold may be a programmable parameter of animplantable system. In another example, the threshold may be scaled onthe basis of a programmable VT parameter that is used to set a beat ratethat the implantable system will treat as a ventricular tachycardiarate.

If the corrected interval is not longer than the threshold, the methodcontinues to the step of combining intervals, as shown at 276, tocorrect for the overdetected event(s). If the corrected interval wouldbe longer than the threshold at step 274, the method simply ends withoutcombining intervals. In this fashion, unnecessary correction of thestored data can be avoided.

FIG. 13 illustrates more methods for inhibiting correlation analysisafter identification of an overdetection. The methods in FIG. 13 takeadvantage of known relationships between the QT interval and the RRinterval of physiologic cardiac cycles. The illustrative method againbegins with the identification of a pattern that suggests overdetection,as indicated at 300. As shown at 302, the possible overdetected event isthen treated as a T-wave (here, the presumption is that a three-eventpattern is identified, with the middle event of the three being thelikely overdetection; other variants may be used) and, as shown at 304,the events on either side of the likely overdetection are treated asR-waves.

These “presumed” R and T waves from steps 302 and 304 are then used toapply a formula for calculating the QT length from the RR interval instep 306. In particular, several likely formulae are shown at 308.Examples include Bazett's formula:

QT(Exp)=QT*√{square root over (RR)}

Friderica's formula:

QT(Exp)=QT*{square root over (RR)}

And the Sagie et al. regression formula:

QT(Exp)=QT+A*(RR−1)

Sagie et al. found A=0.154.

In each formula, the expected QT is shown as QT(exp), the value RR isgiven in seconds, and the value QT is captured during a programmingsession between an implant and a programmer. QT is either captured at oradjusted for a 60 beat-per-minute cardiac rate. The RR interval is foundat step 304, and the measured QT interval can be captured by adding themeasured width of the presumed T-wave to the interval between the firstR-wave and the presumed T-wave.

The expectation is that if the likely overdetected event is anoverdetected T-wave, the measured QT period will match the expected QTvalue given RR, using whichever formula is applied, with some bandallowing for error.

If the formula applied at 306 does not yield a match, no discard occurs,as shown at 310. Alternatively, if the formula applied at 306 yields amatch, then the likely overdetection is discarded as shown at 312. Whenthe likely overdetection is discarded at 312, intervals around theoverdetection are combined, as shown above in FIG. 3B. Once again, theorder of analysis is reversed in other examples.

FIGS. 14A-14B show application of a method illustrated in FIG. 13. Inthe illustrative examples of FIGS. 14A and 14B, Friderica's cube-rootformula is applied. In each example, the previously measured QT=400milliseconds. This value represents the estimated QT interval for thehypothetical patient that would occur at a heart rate of 60 bpm.

Referring to FIG. 14A, given three events X, Y and Z having acorrelation pattern indicating overdetection, the method is applied bypresuming that Y is a T-wave. The QT interval is measured for X and Y,and the RR interval is measured from X to Z, as indicated. The measuredQT is referenced as well, and these values are plugged into the chosenformula. In the example, shown, using RR=0.8 seconds, the expected valuefor QT is 371 milliseconds. Applying a +/−10% error band for thecalculation, the acceptable range is about 334-408 milliseconds for QT.However, as shown, the measured interval is about 500 milliseconds, toolong to be a QT interval for the given parameters. As a result, thecalculation suggests that the Y detection is not an overdetected T-wave,and therefore no data correction occurs. Lesser or greater error bandsizes may be applied; for example, +/−5% error is used in anotherillustrative embodiment.

Referring instead to FIG. 14B, this time, the QT interval measured for Xand Y is about 370 milliseconds. This value falls within the expectedrange, and therefore the calculation suggests that the Y detection is anoverdetected T-wave. Therefore the Y detection is discarded and theinterval data between X and Z is corrected.

In the examples of FIGS. 11-13 and 14A-B, if a likely overdetection isnot discarded, resulting in data correction, the likely overdetectionmay instead be marked as a suspect detection. In an example, suspectdetections are treated as unreliable, both as indicators of cardiacactivity and as endpoints for intervals that can be used in rateanalysis. If the likely overdetection is marked as a suspect detection,the suspect detection and each of the preceding and following intervalsaround the suspect detection are removed from analysis entirely.

FIG. 15 shows a method of analysis for identifying shockable detectedevents and treatable rhythms. FIG. 15 shows the overarching structure ofan analysis method by including the steps of event detection 402, whichis followed by waveform appraisal 404 and beat qualification 406. Inparticular, event detection 402 will typically include monitoring acaptured signal to detect signal amplitude changes that indicate cardiacevents. Once cardiac events are captured at block 402, waveformappraisal 404 can occur. During waveform appraisal 404, thecharacteristics of the signal associated with a detected event areanalyzed to identify and eliminate detected events that are likelycaused by noise or artifacts.

Next, detected events that pass waveform appraisal 404 undergo beatqualification 406, during which detected events are analyzed todetermine whether they display morphology or interval characteristicsthat indicate accurate detection. This may include the correlationanalyses shown above, and/or analysis of intervals or combinations ofthe two, for example analysis to eliminate wide complex double detectioncan use detected event proximity and shape characteristics to identifylikely overdetections. Some further discussion appears in U.S. patentapplication Ser. No. 12/399,914, titled METHODS AND DEVICES FORACCURATELY CLASSIFYING CARDIAC ACTIVITY.

The architecture then turns to rhythm classification, which can begin byconsideration of rate at block 408. If the rate is low, then anindividual detection is marked as “Not Shockable” as indicated at 410.Alternatively, if the rate is very high, it is considered to indicateventricular fibrillation (VF) and therefore is marked as “Shockable,” asshown at 412. Between these low and VF bands of rates is a ventriculartachycardia (VT) zone, and rates in the VT zone are analyzed using whatwill be referred to as Detection Enhancements, as shown at 414.

An example of a Detection Enhancement is as follows:

1. Compare to static template: If Match, not shockable; else

2. Compare to dynamic template: If no Match, shockable event; else

3. Compare to QRS width threshold: If wide, shockable, else notshockable.

Where the dynamic template can be any of the following:

-   -   a) An average of several previous detections that correlate to        one another;    -   b) A set of individual events, for example {N-1 . . . N-i}        wherein matching some or all of the individual events counts as        matching the dynamic template;    -   c) A continually updated template.

The QRS width threshold noted above may be applied in various ways thatcan be tailored to the method of QRS width measurement used in a givensystem and/or that may be tailored to an individual patient. In oneexample, the following rules apply to QRS width:

-   -   x) QRS width, during analysis, is calculated as the duration        from the start of the longest monotonic segment captured during        refractory before the fiducial point to the end of the longest        monotonic segment captured during refractory after the fiducial        point;    -   y) QRS width threshold is measured for the patient during a        programming session, with a maximum allowed value of 113 ms; and    -   z) QRS width during analysis is considered wide if it is at        least 20 ms longer than the QRS width threshold.        These rules x), y) and z) are tailored to one particular        embodiment and may vary depending on the system used.

Following the marking of events as Not Shockable 410 or Shockable 412,an X/Y counter condition is applied as indicated at 416. The X/Y countercondition analyzes the number of Shockable events, X, that are markedduring a previous set, Y, of detected events that pass both waveformappraisal 404 and beat qualification 406. The ratio applied, and setsize used, may vary. One embodiment applies an 18/24 X/Y countercondition at 416. Other embodiments use ratios as 8 or 9 out of 12, 12or 13 out of 16, 24/32, etc.

If the X/Y condition is not met, no shock will be delivered, as shown at418. If the X/Y condition is met, then the method may proceed to acharge confirmation block 420. For example, some embodiments requirethat the X/Y ratio/set size be met for a selected number of consecutiveevents, and this condition may be tested in charge confirmation 420.Another example condition is to determine whether a set, N, ofimmediately preceding detected events are all Shockable, or all haveintervals that are sufficiently short to support a conclusion that thedetected arrhythmia is ongoing. Other factors may also be applied incharge confirmation, for example, by observing whether overdetection hasbeen recently noted (which may suggest that therapy should be delayed toensure that the “arrhythmia” is not a manifestation of overcounting), orobserving whether consistent long intervals have been detected(potentially suggesting spontaneous conversion to normal rhythm by thepatient). For example, charge confirmation 420 may also include methodssuch as those shown in commonly assigned and copending U.S. patentapplication Ser. No. 11/042,911, titled METHOD FOR ADAPTING CHARGEINITIATION FOR AN IMPLANTABLE CARDIOVERTER-DEFIBRILLATOR, the disclosureof which is incorporated herein by reference.

The Charge and Shock block 422 is reached if Charge Confirmation 420 ispassed. Typically the process of charging takes some period of time, andso the method 400 may iterate several times before charging iscompleted. Some or all of the analysis used to reach an initialdetermination that Charging should start may be repeated during thisprocess. Finally, if treatable conditions persist during charging, orare identified following charging, stimulus may be delivered.

With regard to the implantable system, various hardware features may beincorporated. For example, any suitable battery chemistry, such as alithium ion battery, may be used. Therapy output can be created using acapacitive system to store energy until a stimulus level is reachedusing one or several capacitors. A charging circuit such as a flybacktransformer circuit can be used to generate therapy voltages. Therapycan be delivered using, for example, an H-bridge circuit or amodification thereof. Dedicated or general purpose circuitry may be usedto perform analysis functions. For example, a dedicated cardiac signalanalog-to-digital circuit may be used, as well as a dedicatedcorrelation analysis block, as desired, while other functions may beperformed with a microcontroller. Static and dynamic memories may beprovided and used for any suitable functions. These elements may all becomponents of the operational circuitry for the implantable cardiacstimulus system.

Those skilled in the art will recognize that the present invention maybe manifested in a variety of forms other than the specific embodimentsdescribed and contemplated herein. Accordingly, departures in form anddetail may be made without departing from the scope and spirit of thepresent invention.

1. An implantable cardiac stimulus (ICS) system comprising a canisterhousing operational circuitry for the ICS system, the canister having acanister electrode disposed thereon, and a lead having at least a firstlead electrode disposed thereon, the canister electrode and the firstlead electrode electrically coupled to the operational circuitry;wherein the operational circuitry is configured to perform a methodcomprising: detecting electrical events occurring within a patient;constructing event representations for the detected electrical events,the event representations each comprising a series of samples; comparinga first event representation to a template having a template fiducialpoint by identifying an event fiducial point in the first eventrepresentation and aligning the template fiducial point and the eventfiducial point to one another and calculating a first correlationbetween the first event representation and the template when so aligned;again comparing the first event representation to the template, thistime by selecting a different alignment of the template and eventfiducial points; selecting from at least the first correlation or thesecond correlation whichever indicates greater correlation as thecorrelation result for the first event representation; using a series ofcorrelation results to determine whether accurate event detection isoccurring and, if not, correcting data in response to inaccurate eventdetection; determining whether a likely cardiac arrhythmia is occurringand, if so, determining whether stimulus is needed; and if stimulus isneeded, delivering electrical stimulus to the patient from the ICSsystem.
 2. The ICS system of claim 1 wherein the operational circuitryis further configured such that the method also comprises another stepof comparing the first event representation to the template bymisaligning the event fiducial point from the template fiducial pointand calculating a third correlation between the first eventrepresentation and the template; wherein the second correlation iscalculated with the event fiducial point aligned one or more samplesbefore the template fiducial point, and the third correlation iscalculated with the event fiducial point aligned one or more samplesafter the template fiducial point.
 3. The ICS system of claim 1 whereinthe operational circuitry is further configured such that: the secondcorrelation is calculated with the event fiducial point aligned onesample away from the template fiducial point in a first direction; ifthe second correlation is greater than the first correlation, the methodfurther includes yet another step of comparing the first eventrepresentation to the template by aligning the event fiducial point twosamples away from the template fiducial point in the first direction andcalculating a third correlation between the first event representationand the template; or if the second correlation is less than the firstcorrelation, the method further includes the operational circuitryperforming yet another step of comparing the first event representationto the template by aligning the event fiducial point one sample awayfrom the template fiducial point in a direction opposite of the firstdirection and calculating a third correlation between the first eventrepresentation and the template.
 4. The ICS system of claim 1 whereinthe operational circuitry is further configured such that the step ofusing a series of correlation results to determine whether accurateevent detection is occurring comprises determining whether analternating High-Low-High pattern of correlation results occurs and, ifso, determining that an event representation having a Low correlationresult results from an overdetected signal.
 5. The ICS system of claim 1wherein the operational circuitry is further configured such that themethod also comprises determining whether a series of detected eventsare Shockable or Not Shockable by comparing the correlation result forthe first event representation to a threshold and, if the correlationresult exceeds the threshold determining that a detected eventcorresponding to the first event representation is Not Shockable.
 6. Amethod of operation in an implantable cardiac stimulus (ICS) systemcomprising a canister housing operational circuitry for the ICS system,the canister having a canister electrode disposed thereon, and a leadhaving at least a first lead electrode disposed thereon, the canisterelectrode and the first lead electrode electrically coupled to theoperational circuitry; the method comprising: detecting cardiacelectrical events occurring within the patient; constructing eventrepresentations for the detected electrical events; calculating acardiac rate using a set of the detected electrical events; comparingthe event representations to a template representative of a knowncardiac state to establish a set of correlations; analyzing the set ofcorrelations for one of the following: a) a short pattern calling for aset of 3 or more consecutive correlations to include a High-Low-Highpattern around first boundaries, or b) a long pattern calling for a setof 5 or more consecutive correlations to include High-Low-High-Low-Highcorrelations around second boundaries, wherein the first boundariesrequire stronger correlation for “High” than the second boundaries; ifeither the short pattern or the long pattern is identified,characterizing one or more detected events as overdetections andmodifying the calculated cardiac rate; determining whether a likelycardiac arrhythmia is occurring and, if so, determining whether stimulusis needed; and if stimulus is needed, delivering electrical stimulus tothe patient from the ICS system.
 7. The method of claim 6 wherein thesecond boundaries are defined by: identifying a mean correlation in theset of correlations; and establishing an upper bound a set distanceabove the mean correlation.
 8. The method of claim 6 further comprisinganalyzing the set of detections for the following: c) a triplet patterncomprising at least two sequences of High-Low-Low correlations, wherein,if the triplet pattern is identified, the method includes characterizingeach “Low” event as an overdetection and calculating intervals betweenHigh events for use in re-calculating rate.
 9. A method of operation inan implantable cardiac stimulus (ICS) system comprising a canisterhousing operational circuitry for the ICS system, the canister having acanister electrode disposed thereon, and a lead having at least a firstlead electrode disposed thereon, the canister electrode and the firstlead electrode electrically coupled to the operational circuitry; themethod comprising: detecting electrical events occurring within thepatient; calculating a cardiac rate using a set of the detectedelectrical events; identifying one or more detected events as likelyoccurring due to overdetection; analyzing intervals around the eventsidentified as likely occurring due to overdetection to determine whetherthe likely overdetection is likely a T-wave and: a) if the likelyoverdetection is likely a T-wave, correcting data relating to theoverdetection to reduce calculated cardiac rate; or b) if the likelyoverdetection is unlikely to be a T-wave, disabling data correctionmethods in the ICS system for at least the likely overdetection;determining whether a likely cardiac arrhythmia is occurring and, if so,determining whether stimulus is needed; and if stimulus is needed,delivering electrical stimulus to the patient from the ICS system. 10.The method of claim 9 wherein the step of analyzing intervals todetermine whether the likely overdetection is likely a T-wave includesapplying an accepted relationship between QT periods and RR intervals tothe likely overdetection and immediately preceding and followingdetected events as follows: treating the interval for the immediatelypreceding and following detected events as an RR interval; treating theperiod for the immediately preceding detected event and the likelyoverdetection as a QT interval; applying the accepted relationship tothe interval and the period and: if the accepted relationship holds,determining that the likely overdetection is likely to be a T-wave; orif the accepted relationship does not hold, determining that the likelyoverdetection is unlikely to be a T-wave.
 11. The method of claim 10wherein the accepted relationship is chosen from the group consisting ofBazett's formula, Friderica's formula, or a regression formula.
 12. Themethod of claim 9 wherein the step of analyzing intervals to determinewhether the likely overdetection is likely a T-wave includes comparingan interval between the likely overdetection and an immediatelypreceding detected event to a threshold and, if the threshold isexceeded, finding that the likely overdetection is not likely to be aT-wave.
 13. A method of operation in an implantable cardiac stimulus(ICS) system comprising a canister housing operational circuitry for theICS system, the canister having a canister electrode disposed thereon,and a lead having at least a first lead electrode disposed thereon, thecanister electrode and the first lead electrode electrically coupled tothe operational circuitry; the method comprising: detecting electricalevents occurring within the patient; constructing event representationsfor the detected electrical events; for a set of events N, N-1, N-2 andN-3, comparing the Nth event to each of the N-1, N-2 and N-3 events bycorrelation analysis; determining, using the results of the correlationanalysis, whether the set of events indicates one of the following:overdetection, benign cardiac rhythm, or likely cardiac arrhythmia; andif likely cardiac arrhythmia is identified, determining whether stimulusis needed; and if stimulus is needed, delivering electrical stimulus tothe patient from the ICS system; wherein overdetection is identified ifthe correlation analysis indicates a pattern of correlations between theevents N, N-1, N-2 and N-3 that corresponds to overdetection.
 14. Themethod of claim 13 wherein the method includes determining thatoverdetection is occurring if the step of comparing the Nth event toeach of the N-1, N-2 and N-3 events by correlation analysis results in apattern of correlations of the following type: low correlation to N-1and N-3 events, and high correlation to the N-2 event.
 15. The method ofclaim 13 further comprising: calculating a cardiac rate using thedetected electrical events; and if the set of events, N, N-1, N-2, N-3indicates overdetection, correcting data relating to one or moreoverdetected events and recalculating the calculated cardiac rate. 16.The method of claim 13 wherein the step of determining whether the setof events indicates one of the following: overdetection, benign cardiacrhythm, or likely cardiac arrhythmia includes determining whether theNth event correlates highly to at least one of the N-1, N-2 or N-3events and, if so, determining that a benign cardiac rhythm isoccurring.
 17. The method of claim 16 wherein the step of determiningwhether the set of events indicates one of the following: overdetection,benign cardiac rhythm, or likely cardiac arrhythmia includes determiningwhether the Nth event correlates closely to each of the N-1, N-2 or N-3events and, if so, determining that a benign cardiac rhythm isoccurring, further wherein “correlates highly” indicates a relativelyhigher correlation than “correlates closely.”